Swarm vs TaskWeaver
Side-by-side comparison to help you choose.
| Feature | Swarm | TaskWeaver |
|---|---|---|
| Type | Agent | Agent |
| UnfragileRank | 42/100 | 42/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Define AI agents as simple Python objects with static or callable instructions, a list of bound functions, and model configuration. Instructions can be static strings or dynamically generated via callables, enabling context-aware agent behavior without complex inheritance hierarchies. The Agent type (from swarm/types.py) is a minimal data structure that pairs instructions with executable functions, avoiding framework boilerplate while maintaining composability for agent switching.
Unique: Uses callable instructions (functions returning strings) instead of static prompts, enabling instructions to adapt to context variables without re-instantiating agents. This pattern avoids the complexity of prompt engineering frameworks while maintaining dynamic behavior.
vs alternatives: Simpler than LangChain's AgentExecutor or AutoGen's Agent classes because it removes inheritance and configuration complexity, making it ideal for educational purposes and lightweight prototyping.
Maintain and pass context variables (arbitrary Python dictionaries) through agent interactions and handoffs, allowing agents to read and modify shared state. The Swarm.run() method accepts initial context_variables, passes them to all agent functions as parameters, and returns updated context in the response. This enables agents to share information (e.g., user ID, conversation history, flags) without explicit message passing or global state, supporting clean agent-to-agent transitions.
Unique: Context variables are passed as function parameters rather than stored in a centralized context manager, enabling agents to explicitly declare their dependencies and avoid hidden state. This approach mirrors functional programming patterns and makes data flow explicit in code.
vs alternatives: More transparent than AutoGen's ConversableAgent state management because context mutations are explicit in function signatures; lighter-weight than LangChain's memory abstractions because it avoids database/vector store overhead.
Return structured Response objects from Swarm.run() containing the agent's message, updated context variables, and metadata about the execution (e.g., which agent responded, whether a handoff occurred). The Response type encapsulates all relevant information about an agent interaction, enabling applications to inspect and act on execution details beyond just the message text. This pattern supports debugging, logging, and conditional logic based on agent behavior.
Unique: Response objects are simple data structures containing all execution details, enabling transparent inspection of agent behavior. This design avoids hidden state and makes agent interactions auditable and debuggable.
vs alternatives: More transparent than frameworks that hide execution details in logs because Response objects are directly accessible in code; simpler than custom instrumentation because metadata is built-in.
Execute agent interactions synchronously using blocking calls to the OpenAI API, processing one message at a time and waiting for completion before returning. The Swarm.run() method is a blocking function that calls OpenAI's Chat Completions API, processes tool calls, and returns a Response object. This pattern is simple and suitable for single-threaded applications, but can block the event loop in async contexts if not carefully managed.
Unique: Synchronous execution is the default and only mode, keeping the framework simple and suitable for educational purposes. This design avoids async complexity while remaining suitable for most single-threaded use cases.
vs alternatives: Simpler than async frameworks because it avoids event loop management; suitable for educational purposes because control flow is straightforward and debuggable.
Bind Python functions to agents and automatically convert them to OpenAI function-calling schemas (JSON Schema format) for tool invocation. The framework introspects function signatures (using Python's inspect module) to extract parameter names, types, and docstrings, generating tool schemas without manual schema definition. When the LLM requests a tool call, Swarm automatically executes the bound function with the LLM-provided arguments and returns results back to the model, closing the tool-use loop.
Unique: Automatically generates OpenAI function-calling schemas from Python function signatures and docstrings, eliminating manual schema definition. The framework uses Python's inspect module to extract parameter metadata and converts it to JSON Schema, supporting both single and parallel tool calls via tool_choice and parallel_tool_calls agent configuration.
vs alternatives: Reduces boilerplate compared to LangChain's Tool class (which requires manual schema definition) and AutoGen's function registry (which requires explicit tool definitions); tighter integration with OpenAI's native function-calling API.
Enable agents to transfer control to other agents mid-conversation by returning an Agent object from a function call. When an agent function returns an Agent instead of a string, Swarm switches to that agent, preserving the conversation history and context variables. This pattern supports hierarchical workflows (e.g., tier-1 support → tier-2 support → escalation) where agents can decide to hand off based on conversation state, without explicit routing logic in the application layer.
Unique: Handoffs are triggered by agent functions returning Agent objects, making routing decisions explicit and testable. This approach avoids a separate routing layer and keeps handoff logic co-located with the agent that makes the decision, enabling context-aware routing based on conversation state.
vs alternatives: Simpler than AutoGen's nested chat patterns because it doesn't require explicit message passing between agents; more explicit than LangChain's router chains because handoff decisions are made by agent functions, not by a separate routing model.
Stream agent responses token-by-token to the client using OpenAI's streaming API, enabling real-time feedback without waiting for full response completion. The Swarm.run() method supports a stream parameter that yields Response objects containing individual tokens as they arrive from the LLM. This pattern reduces perceived latency in user-facing applications and allows clients to display partial responses while the agent is still thinking, improving user experience in interactive systems.
Unique: Streaming is implemented as a generator pattern in Python, yielding Response objects as tokens arrive. This approach integrates seamlessly with Swarm's existing execution loop and allows clients to consume responses at their own pace without blocking the agent.
vs alternatives: More integrated than manually wrapping OpenAI's streaming API because Swarm handles tool calls and agent switching transparently; simpler than building custom streaming infrastructure on top of the Chat Completions API.
Enable agents to invoke multiple tools in a single turn by setting parallel_tool_calls=True on the Agent configuration. When enabled, the LLM can request multiple tool calls in one response, and Swarm executes all of them concurrently (using Python's asyncio or threading) before returning results back to the model. This pattern reduces round-trips for independent operations (e.g., fetching user data and order history simultaneously) and improves overall agent efficiency.
Unique: Parallel tool calls are configured at the agent level (parallel_tool_calls flag) rather than per-function, enabling the LLM to decide which tools to call in parallel based on conversation context. Swarm handles concurrent execution transparently without requiring developers to write async code.
vs alternatives: Simpler than manually implementing concurrent tool execution with asyncio because Swarm abstracts away concurrency management; more efficient than sequential tool calls because independent operations complete in parallel.
+4 more capabilities
Converts natural language user requests into executable Python code plans by routing through a Planner role that decomposes tasks into sub-steps, then coordinates CodeInterpreter and External Roles to generate and execute code. The Planner maintains a YAML-based prompt configuration that guides task decomposition logic, ensuring structured workflow orchestration rather than free-form text generation. Unlike traditional chat-based agents, TaskWeaver preserves both chat history AND code execution history (including in-memory DataFrames and variables) across stateful sessions.
Unique: Preserves code execution history and in-memory data structures (DataFrames, variables) across multi-turn conversations, enabling true stateful planning where subsequent task decompositions can reference previous results. Most agent frameworks only track text chat history, losing the computational context.
vs alternatives: Outperforms LangChain/LlamaIndex for data analytics workflows because it treats code as the primary communication medium rather than text, enabling direct manipulation of rich data structures without serialization overhead.
The CodeInterpreter role generates Python code based on Planner instructions, then executes it in an isolated sandbox environment with access to a plugin registry. Code generation is guided by available plugins (exposed as callable functions with YAML-defined signatures), and execution results (including variable state and DataFrames) are captured and returned to the Planner. The framework uses a Code Execution Service that manages Python runtime isolation, preventing code injection and enabling safe multi-tenant execution.
Unique: Integrates code generation with a plugin registry system where plugins are exposed as callable Python functions with YAML-defined schemas, enabling the LLM to generate code that calls plugins with proper type signatures. The execution sandbox captures full runtime state (variables, DataFrames) for stateful multi-step workflows.
More robust than Copilot or Cursor for data analytics because it executes generated code in a controlled environment and captures results automatically, rather than requiring manual execution and copy-paste of outputs.
Swarm scores higher at 42/100 vs TaskWeaver at 42/100.
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Supports External Roles (e.g., WebExplorer, ImageReader) that extend TaskWeaver with specialized capabilities beyond code execution. External Roles are implemented as separate modules that communicate with the Planner through the standard message-passing interface, enabling them to be developed and deployed independently. The framework provides a role interface that External Roles must implement, ensuring compatibility with the orchestration system. External Roles can wrap external APIs (web search, image processing services) or custom algorithms, exposing them as callable functions to the CodeInterpreter.
Unique: Enables External Roles (WebExplorer, ImageReader, etc.) to be developed and deployed independently while communicating through the standard Planner interface. This allows specialized capabilities to be added without modifying core framework code.
vs alternatives: More modular than monolithic agent frameworks because External Roles are loosely coupled and can be developed/deployed independently, enabling teams to build specialized capabilities in parallel.
Enables agent behavior customization through YAML configuration files rather than code changes. Configuration files define LLM provider settings, role prompts, plugin registry, execution parameters (timeouts, memory limits), and UI settings. The framework loads configuration at startup and applies it to all components, enabling users to customize agent behavior without modifying Python code. Configuration validation ensures that invalid settings are caught early, preventing runtime errors. Supports environment variable substitution in configuration files for sensitive data (API keys).
Unique: Uses YAML-based configuration files to customize agent behavior (LLM provider, role prompts, plugins, execution parameters) without code changes, enabling easy deployment across environments and experimentation with different settings.
vs alternatives: More flexible than hardcoded agent configurations because all major settings are externalized to YAML, enabling non-developers to customize agent behavior and supporting easy environment-specific deployments.
Provides evaluation and testing capabilities for assessing agent performance on data analytics tasks. The framework includes benchmarks for common analytics workflows and metrics for evaluating task completion, code quality, and execution efficiency. Evaluation can be run against different LLM providers and configurations to compare performance. The testing framework enables developers to write test cases that verify agent behavior on specific tasks, ensuring regressions are caught before deployment. Evaluation results are logged and can be compared across runs to track improvements.
Unique: Provides a built-in evaluation framework for assessing agent performance on data analytics tasks, including benchmarks and metrics for comparing different LLM providers and configurations.
vs alternatives: More comprehensive than ad-hoc testing because it provides standardized benchmarks and metrics for evaluating agent quality, enabling systematic comparison across configurations and tracking improvements over time.
Maintains session state across multiple user interactions by preserving both chat history and code execution history, including in-memory Python objects (DataFrames, variables, function definitions). The Session component manages conversation context, tracks execution artifacts, and enables rollback or reference to previous states. Unlike stateless chat interfaces, TaskWeaver's session model treats the Python runtime as a first-class citizen, allowing subsequent tasks to reference variables or DataFrames created in earlier steps.
Unique: Preserves Python runtime state (variables, DataFrames, function definitions) across multi-turn conversations, not just text chat history. This enables true stateful analytics workflows where a user can reference 'the DataFrame from step 2' without re-running previous code.
vs alternatives: Fundamentally different from stateless LLM chat interfaces (ChatGPT, Claude) because it maintains computational state, enabling iterative data exploration where each step builds on previous results without context loss.
Extends TaskWeaver functionality through a plugin architecture where custom algorithms and tools are wrapped as callable Python functions with YAML-based schema definitions. Plugins define input/output types, parameter constraints, and documentation that the CodeInterpreter uses to generate type-safe function calls. The plugin registry is loaded at startup and exposed to the LLM, enabling code generation that respects function signatures and prevents runtime type errors. Plugins can be domain-specific (e.g., WebExplorer, ImageReader) or custom user-defined functions.
Unique: Uses YAML-based schema definitions for plugins, enabling the LLM to understand function signatures, parameter types, and constraints without inspecting Python code. This allows code generation to be type-aware and prevents runtime errors from type mismatches.
vs alternatives: More structured than LangChain's tool calling because plugins have explicit YAML schemas that the LLM can reason about, rather than relying on docstring parsing or JSON schema inference which is error-prone.
Implements a role-based multi-agent architecture where different agents (Planner, CodeInterpreter, External Roles like WebExplorer, ImageReader) specialize in specific tasks and communicate exclusively through the Planner. The Planner acts as a central hub, routing messages between roles and ensuring coordinated execution. Each role has a specific prompt configuration (defined in YAML) that guides its behavior, and roles communicate through a message-passing system rather than direct function calls. This design enables loose coupling and allows roles to be swapped or extended without modifying the core framework.
Unique: Enforces all inter-role communication through a central Planner rather than allowing direct role-to-role communication. This ensures coordinated execution and prevents agents from operating at cross-purposes, but requires careful Planner prompt engineering to avoid bottlenecks.
vs alternatives: More structured than LangChain's agent composition because roles have explicit responsibilities and communication patterns, reducing the likelihood of agents duplicating work or generating conflicting outputs.
+5 more capabilities